Interpretable ML mechanisms are required under numerous regulatory statutes in the U.S., and explainable ML tools like LIME and other surrogate models, partial dependence plots, and global and local feature importance are already used to document, understand, and validate some predictive models in the financial services industry [1]}, [2]}.REF ,  REF Moreover, adverse action notices are mandated under the Equal Credit Opportunity Act (ECOA) and the Fair Credit Reporting Act (FCRA) for many credit lending, employment, and insurance decisions in the U.S.See: “Adverse Action Notice Requirements Under the ECOA and the FCRA”. If ML is used for such decisions, it must be explained in terms of adverse action notices.This is apparently already happening: “New Patent-Pending Technology from Equifax Enables Configurable AI Models”. Shapley values, and other local feature importance approaches, provide a convenient methodology to rank the direct contribution of input features to final model decisions and potentially generate customer-specific adverse action notices.
